Mapper Configuration — SQLAlchemy 0.8 Documentation

# SQLAlchemy 0.8 Documentation

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Release: 0.8.4 | Release Date: December 8, 2013

# Mapper Configuration

This section describes a variety of configurational patterns that are usable with mappers. It assumes you’ve worked through Object Relational Tutorial and know how to construct and use rudimentary mappers and relationships.

## Classical Mappings

A Classical Mapping refers to the configuration of a mapped class using the `mapper()` function, without using the Declarative system. As an example, start with the declarative mapping introduced in Object Relational Tutorial:

    class User(Base):
        __tablename__ = 'users'

        id = Column(Integer, primary_key=True)
        name = Column(String)
        fullname = Column(String)
        password = Column(String)

In “classical” form, the table metadata is created separately with the `Table` construct, then associated with the `User` class via the `mapper()` function:

    from sqlalchemy import Table, MetaData, Column, ForeignKey, Integer, String
    from sqlalchemy.orm import mapper

    metadata = MetaData()

    user = Table('user', metadata,
                Column('id', Integer, primary_key=True),
                Column('name', String(50)),
                Column('fullname', String(50)),
                Column('password', String(12))
            )

    class User(object):
        def __init__(self, name, fullname, password):
            self.name = name
            self.fullname = fullname
            self.password = password

    mapper(User, user)

Information about mapped attributes, such as relationships to other classes, are provided via the `properties` dictionary. The example below illustrates a second `Table` object, mapped to a class called `Address`, then linked to `User` via `relationship()`:

    address = Table('address', metadata,
                Column('id', Integer, primary_key=True),
                Column('user_id', Integer, ForeignKey('user.id')),
                Column('email_address', String(50))
                )

    mapper(User, user, properties={
        'addresses' : relationship(Address, backref='user', order_by=address.c.id)
    })

    mapper(Address, address)

When using classical mappings, classes must be provided directly without the benefit of the “string lookup” system provided by Declarative. SQL expressions are typically specified in terms of the `Table` objects, i.e. `address.c.id` above for the `Address` relationship, and not `Address.id`, as `Address` may not yet be linked to table metadata, nor can we specify a string here.

Some examples in the documentation still use the classical approach, but note that the classical as well as Declarative approaches are fully interchangeable. Both systems ultimately create the same configuration, consisting of a `Table`, user-defined class, linked together with a `mapper()`. When we talk about “the behavior of `mapper()`”, this includes when using the Declarative system as well - it’s still used, just behind the scenes.

## Customizing Column Properties

The default behavior of `mapper()` is to assemble all the columns in the mapped `Table` into mapped object attributes, each of which are named according to the name of the column itself (specifically, the `key` attribute of `Column`). This behavior can be modified in several ways.

### Naming Columns Distinctly from Attribute Names

A mapping by default shares the same name for a `Column` as that of the mapped attribute - specifically it matches the `Column.key` attribute on `Column`, which by default is the same as the `Column.name`.

The name assigned to the Python attribute which maps to `Column` can be different from either `Column.name` or `Column.key` just by assigning it that way, as we illustrate here in a Declarative mapping:

    class User(Base):
        __tablename__ = 'user'
        id = Column('user_id', Integer, primary_key=True)
        name = Column('user_name', String(50))

Where above `User.id` resolves to a column named `user_id` and `User.name` resolves to a column named `user_name`.

When mapping to an existing table, the `Column` object can be referenced directly:

    class User(Base):
        __table__ = user_table
        id = user_table.c.user_id
        name = user_table.c.user_name

Or in a classical mapping, placed in the `properties` dictionary with the desired key:

    mapper(User, user_table, properties={
       'id': user_table.c.user_id,
       'name': user_table.c.user_name,
    })

In the next section we’ll examine the usage of `.key` more closely.

### Automating Column Naming Schemes from Reflected Tables

In the previous section Naming Columns Distinctly from Attribute Names, we showed how a `Column` explicitly mapped to a class can have a different attribute name than the column. But what if we aren’t listing out `Column` objects explicitly, and instead are automating the production of `Table` objects using reflection (e.g. as described in Reflecting Database Objects)? In this case we can make use of the `DDLEvents.column_reflect()` event to intercept the production of `Column` objects and provide them with the `Column.key` of our choice:

    @event.listens_for(Table, "column_reflect")
    def column_reflect(inspector, table, column_info):
        # set column.key = "attr_<lower_case_name>"
        column_info['key'] = "attr_%s" % column_info['name'].lower()

With the above event, the reflection of `Column` objects will be intercepted with our event that adds a new ”.key” element, such as in a mapping as below:

    class MyClass(Base):
        __table__ = Table("some_table", Base.metadata,
                    autoload=True, autoload_with=some_engine)

If we want to qualify our event to only react for the specific `MetaData` object above, we can check for it in our event:

    @event.listens_for(Table, "column_reflect")
    def column_reflect(inspector, table, column_info):
        if table.metadata is Base.metadata:
            # set column.key = "attr_<lower_case_name>"
            column_info['key'] = "attr_%s" % column_info['name'].lower()

### Naming All Columns with a Prefix

A quick approach to prefix column names, typically when mapping to an existing `Table` object, is to use `column_prefix`:

    class User(Base):
        __table__ = user_table
        __mapper_args__ = {'column_prefix':'_'}

The above will place attribute names such as `_user_id`, `_user_name`, `_password` etc. on the mapped `User` class.

This approach is uncommon in modern usage. For dealing with reflected tables, a more flexible approach is to use that described in Automating Column Naming Schemes from Reflected Tables.

### Using column_property for column level options

Options can be specified when mapping a `Column` using the `column_property()` function. This function explicitly creates the `ColumnProperty` used by the `mapper()` to keep track of the `Column`; normally, the `mapper()` creates this automatically. Using `column_property()`, we can pass additional arguments about how we’d like the `Column` to be mapped. Below, we pass an option `active_history`, which specifies that a change to this column’s value should result in the former value being loaded first:

    from sqlalchemy.orm import column_property

    class User(Base):
        __tablename__ = 'user'

        id = Column(Integer, primary_key=True)
        name = column_property(Column(String(50)), active_history=True)

`column_property()` is also used to map a single attribute to multiple columns. This use case arises when mapping to a `join()` which has attributes which are equated to each other:

    class User(Base):
        __table__ = user.join(address)

        # assign "user.id", "address.user_id" to the
        # "id" attribute
        id = column_property(user_table.c.id, address_table.c.user_id)

For more examples featuring this usage, see Mapping a Class against Multiple Tables.

Another place where `column_property()` is needed is to specify SQL expressions as mapped attributes, such as below where we create an attribute `fullname` that is the string concatenation of the `firstname` and `lastname` columns:

    class User(Base):
        __tablename__ = 'user'
        id = Column(Integer, primary_key=True)
        firstname = Column(String(50))
        lastname = Column(String(50))
        fullname = column_property(firstname + " " + lastname)

See examples of this usage at SQL Expressions as Mapped Attributes.

`sqlalchemy.orm.` `column_property`(*cols, **kw)

  Provide a column-level property for use with a Mapper.

  Column-based properties can normally be applied to the mapper’s `properties` dictionary using the `Column` element directly. Use this function when the given column is not directly present within the mapper’s selectable; examples include SQL expressions, functions, and scalar SELECT queries.

  Columns that aren’t present in the mapper’s selectable won’t be persisted by the mapper and are effectively “read-only” attributes.

  Parameters: | *cols – list of Column objects to be mapped. active_history=False – When `True`, indicates that the “previous” value for a scalar attribute should be loaded when replaced, if not already loaded. Normally, history tracking logic for simple non-primary-key scalar values only needs to be aware of the “new” value in order to perform a flush. This flag is available for applications that make use of `attributes.get_history()` or `Session.is_modified()` which also need to know the “previous” value of the attribute. New in version 0.6.6. comparator_factory – a class which extends `ColumnProperty.Comparator` which provides custom SQL clause generation for comparison operations. group – a group name for this property when marked as deferred. deferred – when True, the column property is “deferred”, meaning that it does not load immediately, and is instead loaded when the attribute is first accessed on an instance. See also `deferred()`. doc – optional string that will be applied as the doc on the class-bound descriptor. expire_on_flush=True – Disable expiry on flush. A column_property() which refers to a SQL expression (and not a single table-bound column) is considered to be a “read only” property; populating it has no effect on the state of data, and it can only return database state. For this reason a column_property()’s value is expired whenever the parent object is involved in a flush, that is, has any kind of “dirty” state within a flush. Setting this parameter to `False` will have the effect of leaving any existing value present after the flush proceeds. Note however that the `Session` with default expiration settings still expires all attributes after a `Session.commit()` call, however. New in version 0.7.3. info – Optional data dictionary which will be populated into the `MapperProperty.info` attribute of this object. New in version 0.8. extension – an `AttributeExtension` instance, or list of extensions, which will be prepended to the list of attribute listeners for the resulting descriptor placed on the class. Deprecated. Please see `AttributeEvents`.

### Mapping a Subset of Table Columns

Sometimes, a `Table` object was made available using the reflection process described at Reflecting Database Objects to load the table’s structure from the database. For such a table that has lots of columns that don’t need to be referenced in the application, the `include_properties` or `exclude_properties` arguments can specify that only a subset of columns should be mapped. For example:

    class User(Base):
        __table__ = user_table
        __mapper_args__ = {
            'include_properties' :['user_id', 'user_name']
        }

...will map the `User` class to the `user_table` table, only including the `user_id` and `user_name` columns - the rest are not referenced. Similarly:

    class Address(Base):
        __table__ = address_table
        __mapper_args__ = {
            'exclude_properties' : ['street', 'city', 'state', 'zip']
        }

...will map the `Address` class to the `address_table` table, including all columns present except `street`, `city`, `state`, and `zip`.

When this mapping is used, the columns that are not included will not be referenced in any SELECT statements emitted by `Query`, nor will there be any mapped attribute on the mapped class which represents the column; assigning an attribute of that name will have no effect beyond that of a normal Python attribute assignment.

In some cases, multiple columns may have the same name, such as when mapping to a join of two or more tables that share some column name. `include_properties` and `exclude_properties` can also accommodate `Column` objects to more accurately describe which columns should be included or excluded:

    class UserAddress(Base):
        __table__ = user_table.join(addresses_table)
        __mapper_args__ = {
            'exclude_properties' :[address_table.c.id],
            'primary_key' : [user_table.c.id]
        }

Note

insert and update defaults configured on individual `Column` objects, i.e. those described at metadata_defaults including those configured by the `default`, `update`, `server_default` and `server_onupdate` arguments, will continue to function normally even if those `Column` objects are not mapped. This is because in the case of `default` and `update`, the `Column` object is still present on the underlying `Table`, thus allowing the default functions to take place when the ORM emits an INSERT or UPDATE, and in the case of `server_default` and `server_onupdate`, the relational database itself maintains these functions.

## Deferred Column Loading

This feature allows particular columns of a table be loaded only upon direct access, instead of when the entity is queried using `Query`. This feature is useful when one wants to avoid loading a large text or binary field into memory when it’s not needed. Individual columns can be lazy loaded by themselves or placed into groups that lazy-load together, using the `orm.deferred()` function to mark them as “deferred”. In the example below, we define a mapping that will load each of `.excerpt` and `.photo` in separate, individual-row SELECT statements when each attribute is first referenced on the individual object instance:

    from sqlalchemy.orm import deferred
    from sqlalchemy import Integer, String, Text, Binary, Column

    class Book(Base):
        __tablename__ = 'book'

        book_id = Column(Integer, primary_key=True)
        title = Column(String(200), nullable=False)
        summary = Column(String(2000))
        excerpt = deferred(Column(Text))
        photo = deferred(Column(Binary))

Classical mappings as always place the usage of `orm.deferred()` in the `properties` dictionary against the table-bound `Column`:

    mapper(Book, book_table, properties={
        'photo':deferred(book_table.c.photo)
    })

Deferred columns can be associated with a “group” name, so that they load together when any of them are first accessed. The example below defines a mapping with a `photos` deferred group. When one `.photo` is accessed, all three photos will be loaded in one SELECT statement. The `.excerpt` will be loaded separately when it is accessed:

    class Book(Base):
        __tablename__ = 'book'

        book_id = Column(Integer, primary_key=True)
        title = Column(String(200), nullable=False)
        summary = Column(String(2000))
        excerpt = deferred(Column(Text))
        photo1 = deferred(Column(Binary), group='photos')
        photo2 = deferred(Column(Binary), group='photos')
        photo3 = deferred(Column(Binary), group='photos')

You can defer or undefer columns at the `Query` level using the `orm.defer()` and `orm.undefer()` query options:

    from sqlalchemy.orm import defer, undefer

    query = session.query(Book)
    query.options(defer('summary')).all()
    query.options(undefer('excerpt')).all()

And an entire “deferred group”, i.e. which uses the `group` keyword argument to `orm.deferred()`, can be undeferred using `orm.undefer_group()`, sending in the group name:

    from sqlalchemy.orm import undefer_group

    query = session.query(Book)
    query.options(undefer_group('photos')).all()

### Column Deferral API

`sqlalchemy.orm.` `deferred`(*columns, **kwargs)

  Return a `DeferredColumnProperty`, which indicates this object attributes should only be loaded from its corresponding table column when first accessed.

  Used with the “properties” dictionary sent to `mapper()`.

  See also:

  Deferred Column Loading

`sqlalchemy.orm.` `defer`(*key)

  Return a `MapperOption` that will convert the column property of the given name into a deferred load.

  Used with `Query.options()`.

  e.g.:

      from sqlalchemy.orm import defer

      query(MyClass).options(defer("attribute_one"),
                          defer("attribute_two"))

  A class bound descriptor is also accepted:

      query(MyClass).options(
                          defer(MyClass.attribute_one),
                          defer(MyClass.attribute_two))

  A “path” can be specified onto a related or collection object using a dotted name. The `orm.defer()` option will be applied to that object when loaded:

      query(MyClass).options(
                          defer("related.attribute_one"),
                          defer("related.attribute_two"))

  To specify a path via class, send multiple arguments:

      query(MyClass).options(
                          defer(MyClass.related, MyOtherClass.attribute_one),
                          defer(MyClass.related, MyOtherClass.attribute_two))

  See also:

  Deferred Column Loading

  Parameters: | *key – A key representing an individual path. Multiple entries are accepted to allow a multiple-token path for a single target, not multiple targets.

`sqlalchemy.orm.` `undefer`(*key)

  Return a `MapperOption` that will convert the column property of the given name into a non-deferred (regular column) load.

  Used with `Query.options()`.

  e.g.:

      from sqlalchemy.orm import undefer

      query(MyClass).options(
                  undefer("attribute_one"),
                  undefer("attribute_two"))

  A class bound descriptor is also accepted:

      query(MyClass).options(
                  undefer(MyClass.attribute_one),
                  undefer(MyClass.attribute_two))

  A “path” can be specified onto a related or collection object using a dotted name. The `orm.undefer()` option will be applied to that object when loaded:

      query(MyClass).options(
                  undefer("related.attribute_one"),
                  undefer("related.attribute_two"))

  To specify a path via class, send multiple arguments:

      query(MyClass).options(
                  undefer(MyClass.related, MyOtherClass.attribute_one),
                  undefer(MyClass.related, MyOtherClass.attribute_two))

  See also:

  `orm.undefer_group()` as a means to “undefer” a group of attributes at once.

  Deferred Column Loading

  Parameters: | *key – A key representing an individual path. Multiple entries are accepted to allow a multiple-token path for a single target, not multiple targets.

`sqlalchemy.orm.` `undefer_group`(name)

  Return a `MapperOption` that will convert the given group of deferred column properties into a non-deferred (regular column) load.

  Used with `Query.options()`.

  e.g.:

      query(MyClass).options(undefer("group_one"))

  See also:

  Deferred Column Loading

  Parameters: | name – String name of the deferred group. This name is established using the “group” name to the `orm.deferred()` configurational function.

## SQL Expressions as Mapped Attributes

Attributes on a mapped class can be linked to SQL expressions, which can be used in queries.

### Using a Hybrid

The easiest and most flexible way to link relatively simple SQL expressions to a class is to use a so-called “hybrid attribute”, described in the section Hybrid Attributes. The hybrid provides for an expression that works at both the Python level as well as at the SQL expression level. For example, below we map a class `User`, containing attributes `firstname` and `lastname`, and include a hybrid that will provide for us the `fullname`, which is the string concatenation of the two:

    from sqlalchemy.ext.hybrid import hybrid_property

    class User(Base):
        __tablename__ = 'user'
        id = Column(Integer, primary_key=True)
        firstname = Column(String(50))
        lastname = Column(String(50))

        @hybrid_property
        def fullname(self):
            return self.firstname + " " + self.lastname

Above, the `fullname` attribute is interpreted at both the instance and class level, so that it is available from an instance:

    some_user = session.query(User).first()
    print some_user.fullname

as well as usable wtihin queries:

    some_user = session.query(User).filter(User.fullname == "John Smith").first()

The string concatenation example is a simple one, where the Python expression can be dual purposed at the instance and class level. Often, the SQL expression must be distinguished from the Python expression, which can be achieved using `hybrid_property.expression()`. Below we illustrate the case where a conditional needs to be present inside the hybrid, using the `if` statement in Python and the `sql.expression.case()` construct for SQL expressions:

    from sqlalchemy.ext.hybrid import hybrid_property
    from sqlalchemy.sql import case

    class User(Base):
        __tablename__ = 'user'
        id = Column(Integer, primary_key=True)
        firstname = Column(String(50))
        lastname = Column(String(50))

        @hybrid_property
        def fullname(self):
            if self.firstname is not None:
                return self.firstname + " " + self.lastname
            else:
                return self.lastname

        @fullname.expression
        def fullname(cls):
            return case([
                (cls.firstname != None, cls.firstname + " " + cls.lastname),
            ], else_ = cls.lastname)

### Using column_property

The `orm.column_property()` function can be used to map a SQL expression in a manner similar to a regularly mapped `Column`. With this technique, the attribute is loaded along with all other column-mapped attributes at load time. This is in some cases an advantage over the usage of hybrids, as the value can be loaded up front at the same time as the parent row of the object, particularly if the expression is one which links to other tables (typically as a correlated subquery) to access data that wouldn’t normally be available on an already loaded object.

Disadvantages to using `orm.column_property()` for SQL expressions include that the expression must be compatible with the SELECT statement emitted for the class as a whole, and there are also some configurational quirks which can occur when using `orm.column_property()` from declarative mixins.

Our “fullname” example can be expressed using `orm.column_property()` as follows:

    from sqlalchemy.orm import column_property

    class User(Base):
        __tablename__ = 'user'
        id = Column(Integer, primary_key=True)
        firstname = Column(String(50))
        lastname = Column(String(50))
        fullname = column_property(firstname + " " + lastname)

Correlated subqueries may be used as well. Below we use the `select()` construct to create a SELECT that links together the count of `Address` objects available for a particular `User`:

    from sqlalchemy.orm import column_property
    from sqlalchemy import select, func
    from sqlalchemy import Column, Integer, String, ForeignKey

    from sqlalchemy.ext.declarative import declarative_base

    Base = declarative_base()

    class Address(Base):
        __tablename__ = 'address'
        id = Column(Integer, primary_key=True)
        user_id = Column(Integer, ForeignKey('user.id'))

    class User(Base):
        __tablename__ = 'user'
        id = Column(Integer, primary_key=True)
        address_count = column_property(
            select([func.count(Address.id)]).\
                where(Address.user_id==id).\
                correlate_except(Address)
        )

In the above example, we define a `select()` construct like the following:

    select([func.count(Address.id)]).\
        where(Address.user_id==id).\
        correlate_except(Address)

The meaning of the above statement is, select the count of `Address.id` rows where the `Address.user_id` column is equated to `id`, which in the context of the `User` class is the `Column` named `id` (note that `id` is also the name of a Python built in function, which is not what we want to use here - if we were outside of the `User` class definition, we’d use `User.id`).

The `select.correlate_except()` directive indicates that each element in the FROM clause of this `select()` may be omitted from the FROM list (that is, correlated to the enclosing SELECT statement against `User`) except for the one corresponding to `Address`. This isn’t strictly necessary, but prevents `Address` from being inadvertently omitted from the FROM list in the case of a long string of joins between `User` and `Address` tables where SELECT statements against `Address` are nested.

If import issues prevent the `column_property()` from being defined inline with the class, it can be assigned to the class after both are configured. In Declarative this has the effect of calling `Mapper.add_property()` to add an additional property after the fact:

    User.address_count = column_property(
            select([func.count(Address.id)]).\
                where(Address.user_id==User.id)
        )

For many-to-many relationships, use `and_()` to join the fields of the association table to both tables in a relation, illustrated here with a classical mapping:

    from sqlalchemy import and_

    mapper(Author, authors, properties={
        'book_count': column_property(
                            select([func.count(books.c.id)],
                                and_(
                                    book_authors.c.author_id==authors.c.id,
                                    book_authors.c.book_id==books.c.id
                                )))
        })

### Using a plain descriptor

In cases where a SQL query more elaborate than what `orm.column_property()` or `hybrid_property` can provide must be emitted, a regular Python function accessed as an attribute can be used, assuming the expression only needs to be available on an already-loaded instance. The function is decorated with Python’s own `@property` decorator to mark it as a read-only attribute. Within the function, `object_session()` is used to locate the `Session` corresponding to the current object, which is then used to emit a query:

    from sqlalchemy.orm import object_session
    from sqlalchemy import select, func

    class User(Base):
        __tablename__ = 'user'
        id = Column(Integer, primary_key=True)
        firstname = Column(String(50))
        lastname = Column(String(50))

        @property
        def address_count(self):
            return object_session(self).\
                scalar(
                    select([func.count(Address.id)]).\
                        where(Address.user_id==self.id)
                )

The plain descriptor approach is useful as a last resort, but is less performant in the usual case than both the hybrid and column property approaches, in that it needs to emit a SQL query upon each access.

## Changing Attribute Behavior

### Simple Validators

A quick way to add a “validation” routine to an attribute is to use the `validates()` decorator. An attribute validator can raise an exception, halting the process of mutating the attribute’s value, or can change the given value into something different. Validators, like all attribute extensions, are only called by normal userland code; they are not issued when the ORM is populating the object:

    from sqlalchemy.orm import validates

    class EmailAddress(Base):
        __tablename__ = 'address'

        id = Column(Integer, primary_key=True)
        email = Column(String)

        @validates('email')
        def validate_email(self, key, address):
            assert '@' in address
            return address

Validators also receive collection events, when items are added to a collection:

    from sqlalchemy.orm import validates

    class User(Base):
        # ...

        addresses = relationship("Address")

        @validates('addresses')
        def validate_address(self, key, address):
            assert '@' in address.email
            return address

Note that the `validates()` decorator is a convenience function built on top of attribute events. An application that requires more control over configuration of attribute change behavior can make use of this system, described at `AttributeEvents`.

`sqlalchemy.orm.` `validates`(*names, **kw)

  Decorate a method as a ‘validator’ for one or more named properties.

  Designates a method as a validator, a method which receives the name of the attribute as well as a value to be assigned, or in the case of a collection, the value to be added to the collection. The function can then raise validation exceptions to halt the process from continuing (where Python’s built-in `ValueError` and `AssertionError` exceptions are reasonable choices), or can modify or replace the value before proceeding. The function should otherwise return the given value.

  Note that a validator for a collection cannot issue a load of that collection within the validation routine - this usage raises an assertion to avoid recursion overflows. This is a reentrant condition which is not supported.

  Parameters: | *names – list of attribute names to be validated. include_removes – if True, “remove” events will be sent as well - the validation function must accept an additional argument “is_remove” which will be a boolean. New in version 0.7.7.

### Using Descriptors and Hybrids

A more comprehensive way to produce modified behavior for an attribute is to use descriptors. These are commonly used in Python using the `property()` function. The standard SQLAlchemy technique for descriptors is to create a plain descriptor, and to have it read/write from a mapped attribute with a different name. Below we illustrate this using Python 2.6-style properties:

    class EmailAddress(Base):
        __tablename__ = 'email_address'

        id = Column(Integer, primary_key=True)

        # name the attribute with an underscore,
        # different from the column name
        _email = Column("email", String)

        # then create an ".email" attribute
        # to get/set "._email"
        @property
        def email(self):
            return self._email

        @email.setter
        def email(self, email):
            self._email = email

The approach above will work, but there’s more we can add. While our `EmailAddress` object will shuttle the value through the `email` descriptor and into the `_email` mapped attribute, the class level `EmailAddress.email` attribute does not have the usual expression semantics usable with `Query`. To provide these, we instead use the `hybrid` extension as follows:

    from sqlalchemy.ext.hybrid import hybrid_property

    class EmailAddress(Base):
        __tablename__ = 'email_address'

        id = Column(Integer, primary_key=True)

        _email = Column("email", String)

        @hybrid_property
        def email(self):
            return self._email

        @email.setter
        def email(self, email):
            self._email = email

The `.email` attribute, in addition to providing getter/setter behavior when we have an instance of `EmailAddress`, also provides a SQL expression when used at the class level, that is, from the `EmailAddress` class directly:

    from sqlalchemy.orm import Session
    session = Session()

    sqladdress = session.query(EmailAddress).\
                     filter(EmailAddress.email == 'address@example.com').\
                     one()
    SELECT address.email AS address_email, address.id AS address_id
    FROM address
    WHERE address.email = ?
    ('address@example.com',)
    address.email = 'otheraddress@example.com'
    sqlsession.commit()
    UPDATE address SET email=? WHERE address.id = ?
    ('otheraddress@example.com', 1)
    COMMIT

The `hybrid_property` also allows us to change the behavior of the attribute, including defining separate behaviors when the attribute is accessed at the instance level versus at the class/expression level, using the `hybrid_property.expression()` modifier. Such as, if we wanted to add a host name automatically, we might define two sets of string manipulation logic:

    class EmailAddress(Base):
        __tablename__ = 'email_address'

        id = Column(Integer, primary_key=True)

        _email = Column("email", String)

        @hybrid_property
        def email(self):
            """Return the value of _email up until the last twelve
            characters."""

            return self._email[:-12]

        @email.setter
        def email(self, email):
            """Set the value of _email, tacking on the twelve character
            value @example.com."""

            self._email = email + "@example.com"

        @email.expression
        def email(cls):
            """Produce a SQL expression that represents the value
            of the _email column, minus the last twelve characters."""

            return func.substr(cls._email, 0, func.length(cls._email) - 12)

Above, accessing the `email` property of an instance of `EmailAddress` will return the value of the `_email` attribute, removing or adding the hostname `@example.com` from the value. When we query against the `email` attribute, a SQL function is rendered which produces the same effect:

    sqladdress = session.query(EmailAddress).filter(EmailAddress.email == 'address').one()
    SELECT address.email AS address_email, address.id AS address_id
    FROM address
    WHERE substr(address.email, ?, length(address.email) - ?) = ?
    (0, 12, 'address')

Read more about Hybrids at Hybrid Attributes.

### Synonyms

Synonyms are a mapper-level construct that applies expression behavior to a descriptor based attribute.

Changed in version 0.7: The functionality of synonym is superceded as of 0.7 by hybrid attributes.

`sqlalchemy.orm.` `synonym`(name, map_column=False, descriptor=None, comparator_factory=None, doc=None)

  Denote an attribute name as a synonym to a mapped property.

  Changed in version 0.7: `synonym()` is superseded by the `hybrid` extension. See the documentation for hybrids at Hybrid Attributes.

  Used with the `properties` dictionary sent to `mapper()`:

      class MyClass(object):
          def _get_status(self):
              return self._status
          def _set_status(self, value):
              self._status = value
          status = property(_get_status, _set_status)

      mapper(MyClass, sometable, properties={
          "status":synonym("_status", map_column=True)
      })

  Above, the `status` attribute of MyClass will produce expression behavior against the table column named `status`, using the Python attribute `_status` on the mapped class to represent the underlying value.

  Parameters: | name – the name of the existing mapped property, which can be any other `MapperProperty` including column-based properties and relationships. map_column – if `True`, an additional `ColumnProperty` is created on the mapper automatically, using the synonym’s name as the keyname of the property, and the keyname of this `synonym()` as the name of the column to map.

### Operator Customization

The “operators” used by the SQLAlchemy ORM and Core expression language are fully customizable. For example, the comparison expression `User.name == 'ed'` makes usage of an operator built into Python itself called `operator.eq` - the actual SQL construct which SQLAlchemy associates with such an operator can be modified. New operations can be associated with column expressions as well. The operators which take place for column expressions are most directly redefined at the type level - see the section Redefining and Creating New Operators for a description.

ORM level functions like `column_property()`, `relationship()`, and `composite()` also provide for operator redefinition at the ORM level, by passing a `PropComparator` subclass to the `comparator_factory` argument of each function. Customization of operators at this level is a rare use case. See the documentation at `PropComparator` for an overview.

## Composite Column Types

Sets of columns can be associated with a single user-defined datatype. The ORM provides a single attribute which represents the group of columns using the class you provide.

Changed in version 0.7: Composites have been simplified such that they no longer “conceal” the underlying column based attributes. Additionally, in-place mutation is no longer automatic; see the section below on enabling mutability to support tracking of in-place changes.

A simple example represents pairs of columns as a `Point` object. `Point` represents such a pair as `.x` and `.y`:

    class Point(object):
        def __init__(self, x, y):
            self.x = x
            self.y = y

        def __composite_values__(self):
            return self.x, self.y

        def __repr__(self):
            return "Point(x=%r, y=%r)" % (self.x, self.y)

        def __eq__(self, other):
            return isinstance(other, Point) and \
                other.x == self.x and \
                other.y == self.y

        def __ne__(self, other):
            return not self.__eq__(other)

The requirements for the custom datatype class are that it have a constructor which accepts positional arguments corresponding to its column format, and also provides a method `__composite_values__()` which returns the state of the object as a list or tuple, in order of its column-based attributes. It also should supply adequate `__eq__()` and `__ne__()` methods which test the equality of two instances.

We will create a mapping to a table `vertice`, which represents two points as `x1/y1` and `x2/y2`. These are created normally as `Column` objects. Then, the `composite()` function is used to assign new attributes that will represent sets of columns via the `Point` class:

    from sqlalchemy import Column, Integer
    from sqlalchemy.orm import composite
    from sqlalchemy.ext.declarative import declarative_base

    Base = declarative_base()

    class Vertex(Base):
        __tablename__ = 'vertice'

        id = Column(Integer, primary_key=True)
        x1 = Column(Integer)
        y1 = Column(Integer)
        x2 = Column(Integer)
        y2 = Column(Integer)

        start = composite(Point, x1, y1)
        end = composite(Point, x2, y2)

A classical mapping above would define each `composite()` against the existing table:

    mapper(Vertex, vertice_table, properties={
        'start':composite(Point, vertice_table.c.x1, vertice_table.c.y1),
        'end':composite(Point, vertice_table.c.x2, vertice_table.c.y2),
    })

We can now persist and use `Vertex` instances, as well as query for them, using the `.start` and `.end` attributes against ad-hoc `Point` instances:

    >>> v = Vertex(start=Point(3, 4), end=Point(5, 6))
    >>> session.add(v)
    >>> q = session.query(Vertex).filter(Vertex.start == Point(3, 4))
    sql>>> print q.first().start
    BEGIN (implicit)
    INSERT INTO vertice (x1, y1, x2, y2) VALUES (?, ?, ?, ?)
    (3, 4, 5, 6)
    SELECT vertice.id AS vertice_id,
            vertice.x1 AS vertice_x1,
            vertice.y1 AS vertice_y1,
            vertice.x2 AS vertice_x2,
            vertice.y2 AS vertice_y2
    FROM vertice
    WHERE vertice.x1 = ? AND vertice.y1 = ?
     LIMIT ? OFFSET ?
    (3, 4, 1, 0)Point(x=3, y=4)

`sqlalchemy.orm.` `composite`(class_, *cols, **kwargs)

  Return a composite column-based property for use with a Mapper.

  See the mapping documentation section Composite Column Types for a full usage example.

  The `MapperProperty` returned by `composite()` is the `CompositeProperty`.

  Parameters: | class_ – The “composite type” class. *cols – List of Column objects to be mapped. active_history=False – When `True`, indicates that the “previous” value for a scalar attribute should be loaded when replaced, if not already loaded. See the same flag on `column_property()`. Changed in version 0.7: This flag specifically becomes meaningful - previously it was a placeholder. group – A group name for this property when marked as deferred. deferred – When True, the column property is “deferred”, meaning that it does not load immediately, and is instead loaded when the attribute is first accessed on an instance. See also `deferred()`. comparator_factory – a class which extends `CompositeProperty.Comparator` which provides custom SQL clause generation for comparison operations. doc – optional string that will be applied as the doc on the class-bound descriptor. info – Optional data dictionary which will be populated into the `MapperProperty.info` attribute of this object. New in version 0.8. extension – an `AttributeExtension` instance, or list of extensions, which will be prepended to the list of attribute listeners for the resulting descriptor placed on the class. Deprecated. Please see `AttributeEvents`.

### Tracking In-Place Mutations on Composites

In-place changes to an existing composite value are not tracked automatically. Instead, the composite class needs to provide events to its parent object explicitly. This task is largely automated via the usage of the `MutableComposite` mixin, which uses events to associate each user-defined composite object with all parent associations. Please see the example in Establishing Mutability on Composites.

Changed in version 0.7: In-place changes to an existing composite value are no longer tracked automatically; the functionality is superseded by the `MutableComposite` class.

### Redefining Comparison Operations for Composites

The “equals” comparison operation by default produces an AND of all corresponding columns equated to one another. This can be changed using the `comparator_factory` argument to `composite()`, where we specify a custom `CompositeProperty.Comparator` class to define existing or new operations. Below we illustrate the “greater than” operator, implementing the same expression that the base “greater than” does:

    from sqlalchemy.orm.properties import CompositeProperty
    from sqlalchemy import sql

    class PointComparator(CompositeProperty.Comparator):
        def __gt__(self, other):
            """redefine the 'greater than' operation"""

            return sql.and_(*[a>b for a, b in
                              zip(self.__clause_element__().clauses,
                                  other.__composite_values__())])

    class Vertex(Base):
        ___tablename__ = 'vertice'

        id = Column(Integer, primary_key=True)
        x1 = Column(Integer)
        y1 = Column(Integer)
        x2 = Column(Integer)
        y2 = Column(Integer)

        start = composite(Point, x1, y1,
                            comparator_factory=PointComparator)
        end = composite(Point, x2, y2,
                            comparator_factory=PointComparator)

## Mapping a Class against Multiple Tables

Mappers can be constructed against arbitrary relational units (called selectables) in addition to plain tables. For example, the `join()` function creates a selectable unit comprised of multiple tables, complete with its own composite primary key, which can be mapped in the same way as a `Table`:

    from sqlalchemy import Table, Column, Integer, \
            String, MetaData, join, ForeignKey
    from sqlalchemy.ext.declarative import declarative_base
    from sqlalchemy.orm import column_property

    metadata = MetaData()

    # define two Table objects
    user_table = Table('user', metadata,
                Column('id', Integer, primary_key=True),
                Column('name', String),
            )

    address_table = Table('address', metadata,
                Column('id', Integer, primary_key=True),
                Column('user_id', Integer, ForeignKey('user.id')),
                Column('email_address', String)
                )

    # define a join between them.  This
    # takes place across the user.id and address.user_id
    # columns.
    user_address_join = join(user_table, address_table)

    Base = declarative_base()

    # map to it
    class AddressUser(Base):
        __table__ = user_address_join

        id = column_property(user_table.c.id, address_table.c.user_id)
        address_id = address_table.c.id

In the example above, the join expresses columns for both the `user` and the `address` table. The `user.id` and `address.user_id` columns are equated by foreign key, so in the mapping they are defined as one attribute, `AddressUser.id`, using `column_property()` to indicate a specialized column mapping. Based on this part of the configuration, the mapping will copy new primary key values from `user.id` into the `address.user_id` column when a flush occurs.

Additionally, the `address.id` column is mapped explicitly to an attribute named `address_id`. This is to disambiguate the mapping of the `address.id` column from the same-named `AddressUser.id` attribute, which here has been assigned to refer to the `user` table combined with the `address.user_id` foreign key.

The natural primary key of the above mapping is the composite of `(user.id, address.id)`, as these are the primary key columns of the `user` and `address` table combined together. The identity of an `AddressUser` object will be in terms of these two values, and is represented from an `AddressUser` object as `(AddressUser.id, AddressUser.address_id)`.

## Mapping a Class against Arbitrary Selects

Similar to mapping against a join, a plain `select()` object can be used with a mapper as well. The example fragment below illustrates mapping a class called `Customer` to a `select()` which includes a join to a subquery:

    from sqlalchemy import select, func

    subq = select([
                func.count(orders.c.id).label('order_count'),
                func.max(orders.c.price).label('highest_order'),
                orders.c.customer_id
                ]).group_by(orders.c.customer_id).alias()

    customer_select = select([customers, subq]).\
                select_from(
                    join(customers, subq,
                            customers.c.id == subq.c.customer_id)
                ).alias()

    class Customer(Base):
        __table__ = customer_select

Above, the full row represented by `customer_select` will be all the columns of the `customers` table, in addition to those columns exposed by the `subq` subquery, which are `order_count`, `highest_order`, and `customer_id`. Mapping the `Customer` class to this selectable then creates a class which will contain those attributes.

When the ORM persists new instances of `Customer`, only the `customers` table will actually receive an INSERT. This is because the primary key of the `orders` table is not represented in the mapping; the ORM will only emit an INSERT into a table for which it has mapped the primary key.

Note

The practice of mapping to arbitrary SELECT statements, especially complex ones as above, is almost never needed; it necessarily tends to produce complex queries which are often less efficient than that which would be produced by direct query construction. The practice is to some degree based on the very early history of SQLAlchemy where the `mapper()` construct was meant to represent the primary querying interface; in modern usage, the `Query` object can be used to construct virtually any SELECT statement, including complex composites, and should be favored over the “map-to-selectable” approach.

## Multiple Mappers for One Class

In modern SQLAlchemy, a particular class is only mapped by one `mapper()` at a time. The rationale here is that the `mapper()` modifies the class itself, not only persisting it towards a particular `Table`, but also instrumenting attributes upon the class which are structured specifically according to the table metadata.

One potential use case for another mapper to exist at the same time is if we wanted to load instances of our class not just from the immediate `Table` to which it is mapped, but from another selectable that is a derivation of that `Table`. While there technically is a way to create such a `mapper()`, using the `non_primary=True` option, this approach is virtually never needed. Instead, we use the functionality of the `Query` object to achieve this, using a method such as `Query.select_from()` or `Query.from_statement()` to specify a derived selectable.

Another potential use is if we genuinely want instances of our class to be persisted into different tables at different times; certain kinds of data sharding configurations may persist a particular class into tables that are identical in structure except for their name. For this kind of pattern, Python offers a better approach than the complexity of mapping the same class multiple times, which is to instead create new mapped classes for each target table. SQLAlchemy refers to this as the “entity name” pattern, which is described as a recipe at Entity Name.

## Constructors and Object Initialization

Mapping imposes no restrictions or requirements on the constructor (`__init__`) method for the class. You are free to require any arguments for the function that you wish, assign attributes to the instance that are unknown to the ORM, and generally do anything else you would normally do when writing a constructor for a Python class.

The SQLAlchemy ORM does not call `__init__` when recreating objects from database rows. The ORM’s process is somewhat akin to the Python standard library’s `pickle` module, invoking the low level `__new__` method and then quietly restoring attributes directly on the instance rather than calling `__init__`.

If you need to do some setup on database-loaded instances before they’re ready to use, you can use the `@reconstructor` decorator to tag a method as the ORM counterpart to `__init__`. SQLAlchemy will call this method with no arguments every time it loads or reconstructs one of your instances. This is useful for recreating transient properties that are normally assigned in your `__init__`:

    from sqlalchemy import orm

    class MyMappedClass(object):
        def __init__(self, data):
            self.data = data
            # we need stuff on all instances, but not in the database.
            self.stuff = []

        @orm.reconstructor
        def init_on_load(self):
            self.stuff = []

When `obj = MyMappedClass()` is executed, Python calls the `__init__` method as normal and the `data` argument is required. When instances are loaded during a `Query` operation as in `query(MyMappedClass).one()`, `init_on_load` is called.

Any method may be tagged as the `reconstructor()`, even the `__init__` method. SQLAlchemy will call the reconstructor method with no arguments. Scalar (non-collection) database-mapped attributes of the instance will be available for use within the function. Eagerly-loaded collections are generally not yet available and will usually only contain the first element. ORM state changes made to objects at this stage will not be recorded for the next flush() operation, so the activity within a reconstructor should be conservative.

`reconstructor()` is a shortcut into a larger system of “instance level” events, which can be subscribed to using the event API - see `InstanceEvents` for the full API description of these events.

`sqlalchemy.orm.` `reconstructor`(fn)

  Decorate a method as the ‘reconstructor’ hook.

  Designates a method as the “reconstructor”, an `__init__`-like method that will be called by the ORM after the instance has been loaded from the database or otherwise reconstituted.

  The reconstructor will be invoked with no arguments. Scalar (non-collection) database-mapped attributes of the instance will be available for use within the function. Eagerly-loaded collections are generally not yet available and will usually only contain the first element. ORM state changes made to objects at this stage will not be recorded for the next flush() operation, so the activity within a reconstructor should be conservative.

## Configuring a Version Counter

The `Mapper` supports management of a version id column, which is a single table column that increments or otherwise updates its value each time an `UPDATE` to the mapped table occurs. This value is checked each time the ORM emits an `UPDATE` or `DELETE` against the row to ensure that the value held in memory matches the database value.

The purpose of this feature is to detect when two concurrent transactions are modifying the same row at roughly the same time, or alternatively to provide a guard against the usage of a “stale” row in a system that might be re-using data from a previous transaction without refreshing (e.g. if one sets `expire_on_commit=False` with a `Session`, it is possible to re-use the data from a previous transaction).

Concurrent transaction updates

When detecting concurrent updates within transactions, it is typically the case that the database’s transaction isolation level is below the level of repeatable read; otherwise, the transaction will not be exposed to a new row value created by a concurrent update which conflicts with the locally updated value. In this case, the SQLAlchemy versioning feature will typically not be useful for in-transaction conflict detection, though it still can be used for cross-transaction staleness detection.

The database that enforces repeatable reads will typically either have locked the target row against a concurrent update, or is employing some form of multi version concurrency control such that it will emit an error when the transaction is committed. SQLAlchemy’s version_id_col is an alternative which allows version tracking to occur for specific tables within a transaction that otherwise might not have this isolation level set.

See also

Repeatable Read Isolation Level - Postgresql’s implementation of repeatable read, including a description of the error condition.

### Simple Version Counting

The most straightforward way to track versions is to add an integer column to the mapped table, then establish it as the `version_id_col` within the mapper options:

    class User(Base):
        __tablename__ = 'user'

        id = Column(Integer, primary_key=True)
        version_id = Column(Integer, nullable=False)
        name = Column(String(50), nullable=False)

        __mapper_args__ = {
            "version_id_col": version_id
        }

Above, the `User` mapping tracks integer versions using the column `version_id`. When an object of type `User` is first flushed, the `version_id` column will be given a value of “1”. Then, an UPDATE of the table later on will always be emitted in a manner similar to the following:

    UPDATE user SET version_id=:version_id, name=:name
    WHERE user.id = :user_id AND user.version_id = :user_version_id
    {"name": "new name", "version_id": 2, "user_id": 1, "user_version_id": 1}

The above UPDATE statement is updating the row that not only matches `user.id = 1`, it also is requiring that `user.version_id = 1`, where “1” is the last version identifier we’ve been known to use on this object. If a transaction elsewhere has modifed the row independently, this version id will no longer match, and the UPDATE statement will report that no rows matched; this is the condition that SQLAlchemy tests, that exactly one row matched our UPDATE (or DELETE) statement. If zero rows match, that indicates our version of the data is stale, and a `StaleDataError` is raised.

### Custom Version Counters / Types

Other kinds of values or counters can be used for versioning. Common types include dates and GUIDs. When using an alternate type or counter scheme, SQLAlchemy provides a hook for this scheme using the `version_id_generator` argument, which accepts a version generation callable. This callable is passed the value of the current known version, and is expected to return the subsequent version.

For example, if we wanted to track the versioning of our `User` class using a randomly generated GUID, we could do this (note that some backends support a native GUID type, but we illustrate here using a simple string):

    import uuid

    class User(Base):
        __tablename__ = 'user'

        id = Column(Integer, primary_key=True)
        version_uuid = Column(String(32))
        name = Column(String(50), nullable=False)

        __mapper_args__ = {
            'version_id_col':version_uuid,
            'version_id_generator':lambda version: uuid.uuid4().hex
        }

The persistence engine will call upon `uuid.uuid4()` each time a `User` object is subject to an INSERT or an UPDATE. In this case, our version generation function can disregard the incoming value of `version`, as the `uuid4()` function generates identifiers without any prerequisite value. If we were using a sequential versioning scheme such as numeric or a special character system, we could make use of the given `version` in order to help determine the subsequent value.

See also

Backend-agnostic GUID Type

## Class Mapping API

`sqlalchemy.orm.` `mapper`(class_, local_table=None, *args, **params)

  Return a new `Mapper` object.

  This function is typically used behind the scenes via the Declarative extension. When using Declarative, many of the usual `mapper()` arguments are handled by the Declarative extension itself, including `class_`, `local_table`, `properties`, and `inherits`. Other options are passed to `mapper()` using the `__mapper_args__` class variable:

      class MyClass(Base):
          __tablename__ = 'my_table'
          id = Column(Integer, primary_key=True)
          type = Column(String(50))
          alt = Column("some_alt", Integer)

          __mapper_args__ = {
              'polymorphic_on' : type
          }

  Explicit use of `mapper()` is often referred to as classical mapping. The above declarative example is equivalent in classical form to:

      my_table = Table("my_table", metadata,
          Column('id', Integer, primary_key=True),
          Column('type', String(50)),
          Column("some_alt", Integer)
      )

      class MyClass(object):
          pass

      mapper(MyClass, my_table,
          polymorphic_on=my_table.c.type,
          properties={
              'alt':my_table.c.some_alt
          })

  See also:

  Classical Mappings - discussion of direct usage of `mapper()`

  - Parameters: | class_ – The class to be mapped. When using Declarative, this argument is automatically passed as the declared class itself. local_table – The `Table` or other selectable to which the class is mapped. May be `None` if this mapper inherits from another mapper using single-table inheritance. When using Declarative, this argument is automatically passed by the extension, based on what is configured via the `__table__` argument or via the `Table` produced as a result of the `__tablename__` and `Column` arguments present. always_refresh – If True, all query operations for this mapped class will overwrite all data within object instances that already exist within the session, erasing any in-memory changes with whatever information was loaded from the database. Usage of this flag is highly discouraged; as an alternative, see the method `Query.populate_existing()`. allow_partial_pks – Defaults to True. Indicates that a composite primary key with some NULL values should be considered as possibly existing within the database. This affects whether a mapper will assign an incoming row to an existing identity, as well as if `Session.merge()` will check the database first for a particular primary key value. A “partial primary key” can occur if one has mapped to an OUTER JOIN, for example. batch – Defaults to `True`, indicating that save operations of multiple entities can be batched together for efficiency. Setting to False indicates that an instance will be fully saved before saving the next instance. This is used in the extremely rare case that a `MapperEvents` listener requires being called in between individual row persistence operations. column_prefix – A string which will be prepended to the mapped attribute name when `Column` objects are automatically assigned as attributes to the mapped class. Does not affect explicitly specified column-based properties. See the section Naming All Columns with a Prefix for an example. concrete – If True, indicates this mapper should use concrete table inheritance with its parent mapper. See the section Concrete Table Inheritance for an example. eager_defaults – if True, the ORM will immediately fetch the value of server-generated default values after an INSERT or UPDATE, rather than leaving them as expired to be fetched on next access. This can be used for event schemes where the server-generated values are needed immediately before the flush completes. This scheme will emit an individual `SELECT` statement per row inserted or updated, which note can add significant performance overhead. exclude_properties – A list or set of string column names to be excluded from mapping. See Mapping a Subset of Table Columns for an example. extension – A `MapperExtension` instance or list of `MapperExtension` instances which will be applied to all operations by this `Mapper`. Deprecated. Please see `MapperEvents`. include_properties – An inclusive list or set of string column names to map. See Mapping a Subset of Table Columns for an example. inherits – A mapped class or the corresponding `Mapper` of one indicating a superclass to which this `Mapper` should inherit from. The mapped class here must be a subclass of the other mapper’s class. When using Declarative, this argument is passed automatically as a result of the natural class hierarchy of the declared classes. See also Mapping Class Inheritance Hierarchies inherit_condition – For joined table inheritance, a SQL expression which will define how the two tables are joined; defaults to a natural join between the two tables. inherit_foreign_keys – When `inherit_condition` is used and the columns present are missing a `ForeignKey` configuration, this parameter can be used to specify which columns are “foreign”. In most cases can be left as `None`. legacy_is_orphan – Boolean, defaults to `False`. When `True`, specifies that “legacy” orphan consideration is to be applied to objects mapped by this mapper, which means that a pending (that is, not persistent) object is auto-expunged from an owning `Session` only when it is de-associated from all parents that specify a `delete-orphan` cascade towards this mapper. The new default behavior is that the object is auto-expunged when it is de-associated with any of its parents that specify `delete-orphan` cascade. This behavior is more consistent with that of a persistent object, and allows behavior to be consistent in more scenarios independently of whether or not an orphanable object has been flushed yet or not. See the change note and example at The consideration of a “pending” object as an “orphan” has been made more aggressive for more detail on this change. New in version 0.8: - the consideration of a pending object as an “orphan” has been modified to more closely match the behavior as that of persistent objects, which is that the object is expunged from the `Session` as soon as it is de-associated from any of its orphan-enabled parents. Previously, the pending object would be expunged only if de-associated from all of its orphan-enabled parents. The new flag `legacy_is_orphan` is added to `orm.mapper()` which re-establishes the legacy behavior. non_primary – Specify that this `Mapper` is in addition to the “primary” mapper, that is, the one used for persistence. The `Mapper` created here may be used for ad-hoc mapping of the class to an alternate selectable, for loading only. The `non_primary` feature is rarely needed with modern usage. order_by – A single `Column` or list of `Column` objects for which selection operations should use as the default ordering for entities. By default mappers have no pre-defined ordering. passive_updates – Indicates UPDATE behavior of foreign key columns when a primary key column changes on a joined-table inheritance mapping. Defaults to `True`. When True, it is assumed that ON UPDATE CASCADE is configured on the foreign key in the database, and that the database will handle propagation of an UPDATE from a source column to dependent columns on joined-table rows. When False, it is assumed that the database does not enforce referential integrity and will not be issuing its own CASCADE operation for an update. The `Mapper` here will emit an UPDATE statement for the dependent columns during a primary key change. See also Mutable Primary Keys / Update Cascades - description of a similar feature as used with `relationship()` polymorphic_on – Specifies the column, attribute, or SQL expression used to determine the target class for an incoming row, when inheriting classes are present. This value is commonly a `Column` object that’s present in the mapped `Table`:
        class Employee(Base):
            __tablename__ = 'employee'

            id = Column(Integer, primary_key=True)
            discriminator = Column(String(50))

            __mapper_args__ = {
                "polymorphic_on":discriminator,
                "polymorphic_identity":"employee"
            }
    It may also be specified as a SQL expression, as in this example where we use the `case()` construct to provide a conditional approach:
        class Employee(Base):
            __tablename__ = 'employee'

            id = Column(Integer, primary_key=True)
            discriminator = Column(String(50))

            __mapper_args__ = {
                "polymorphic_on":case([
                    (discriminator == "EN", "engineer"),
                    (discriminator == "MA", "manager"),
                ], else_="employee"),
                "polymorphic_identity":"employee"
            }
    It may also refer to any attribute configured with `column_property()`, or to the string name of one:
        class Employee(Base):
            __tablename__ = 'employee'

            id = Column(Integer, primary_key=True)
            discriminator = Column(String(50))
            employee_type = column_property(
                case([
                    (discriminator == "EN", "engineer"),
                    (discriminator == "MA", "manager"),
                ], else_="employee")
            )

            __mapper_args__ = {
                "polymorphic_on":employee_type,
                "polymorphic_identity":"employee"
            }
    Changed in version 0.7.4: `polymorphic_on` may be specified as a SQL expression, or refer to any attribute configured with `column_property()`, or to the string name of one. When setting `polymorphic_on` to reference an attribute or expression that’s not present in the locally mapped `Table`, yet the value of the discriminator should be persisted to the database, the value of the discriminator is not automatically set on new instances; this must be handled by the user, either through manual means or via event listeners. A typical approach to establishing such a listener looks like:
        from sqlalchemy import event
        from sqlalchemy.orm import object_mapper

        @event.listens_for(Employee, "init", propagate=True)
        def set_identity(instance, *arg, **kw):
            mapper = object_mapper(instance)
            instance.discriminator = mapper.polymorphic_identity
  - Where above, we assign the value of `polymorphic_identity` for the mapped class to the `discriminator` attribute, thus persisting the value to the `discriminator` column in the database. See also Mapping Class Inheritance Hierarchies polymorphic_identity – Specifies the value which identifies this particular class as returned by the column expression referred to by the `polymorphic_on` setting. As rows are received, the value corresponding to the `polymorphic_on` column expression is compared to this value, indicating which subclass should be used for the newly reconstructed object. properties – A dictionary mapping the string names of object attributes to `MapperProperty` instances, which define the persistence behavior of that attribute. Note that `Column` objects present in the mapped `Table` are automatically placed into `ColumnProperty` instances upon mapping, unless overridden. When using Declarative, this argument is passed automatically, based on all those `MapperProperty` instances declared in the declared class body. primary_key – A list of `Column` objects which define the primary key to be used against this mapper’s selectable unit. This is normally simply the primary key of the `local_table`, but can be overridden here. version_id_col – A `Column` that will be used to keep a running version id of rows in the table. This is used to detect concurrent updates or the presence of stale data in a flush. The methodology is to detect if an UPDATE statement does not match the last known version id, a `StaleDataError` exception is thrown. By default, the column must be of `Integer` type, unless `version_id_generator` specifies an alternative version generator. See also Configuring a Version Counter - discussion of version counting and rationale. version_id_generator – Define how new version ids should be generated. Defaults to `None`, which indicates that a simple integer counting scheme be employed. To provide a custom versioning scheme, provide a callable function of the form:
        def generate_version(version):
            return next_version
  See also Custom Version Counters / Types with_polymorphic – A tuple in the form `(<classes>, <selectable>)` indicating the default style of “polymorphic” loading, that is, which tables are queried at once. <classes> is any single or list of mappers and/or classes indicating the inherited classes that should be loaded at once. The special value `'*'` may be used to indicate all descending classes should be loaded immediately. The second tuple argument <selectable> indicates a selectable that will be used to query for multiple classes. See also Basic Control of Which Tables are Queried

`sqlalchemy.orm.` `object_mapper`(instance)

  Given an object, return the primary Mapper associated with the object instance.

  Raises `sqlalchemy.orm.exc.UnmappedInstanceError` if no mapping is configured.

  This function is available via the inspection system as:

      inspect(instance).mapper

  Using the inspection system will raise `sqlalchemy.exc.NoInspectionAvailable` if the instance is not part of a mapping.

`sqlalchemy.orm.` `class_mapper`(class_, configure=True)

  Given a class, return the primary `Mapper` associated with the key.

  Raises `UnmappedClassError` if no mapping is configured on the given class, or `ArgumentError` if a non-class object is passed.

  Equivalent functionality is available via the `inspect()` function as:

      inspect(some_mapped_class)

  Using the inspection system will raise `sqlalchemy.exc.NoInspectionAvailable` if the class is not mapped.

`sqlalchemy.orm.` `configure_mappers`()

  Initialize the inter-mapper relationships of all mappers that have been constructed thus far.

  This function can be called any number of times, but in most cases is handled internally.

`sqlalchemy.orm.` `clear_mappers`()

  Remove all mappers from all classes.

  This function removes all instrumentation from classes and disposes of their associated mappers. Once called, the classes are unmapped and can be later re-mapped with new mappers.

  `clear_mappers()` is not for normal use, as there is literally no valid usage for it outside of very specific testing scenarios. Normally, mappers are permanent structural components of user-defined classes, and are never discarded independently of their class. If a mapped class itself is garbage collected, its mapper is automatically disposed of as well. As such, `clear_mappers()` is only for usage in test suites that re-use the same classes with different mappings, which is itself an extremely rare use case - the only such use case is in fact SQLAlchemy’s own test suite, and possibly the test suites of other ORM extension libraries which intend to test various combinations of mapper construction upon a fixed set of classes.

`sqlalchemy.orm.util.` `identity_key`(*args, **kwargs)

  Generate “identity key” tuples, as are used as keys in the `Session.identity_map` dictionary.

  This function has several call styles:

  - `identity_key(class, ident)`

    This form receives a mapped class and a primary key scalar or tuple as an argument.

    E.g.:

        >>> identity_key(MyClass, (1, 2))
        (<class '__main__.MyClass'>, (1, 2))

    param class: | mapped class (must be a positional argument)
    param ident: | primary key, may be a scalar or tuple argument.

  - `identity_key(instance=instance)`

    This form will produce the identity key for a given instance. The instance need not be persistent, only that its primary key attributes are populated (else the key will contain `None` for those missing values).

    E.g.:

        >>> instance = MyClass(1, 2)
        >>> identity_key(instance=instance)
        (<class '__main__.MyClass'>, (1, 2))

    In this form, the given instance is ultimately run though `Mapper.identity_key_from_instance()`, which will have the effect of performing a database check for the corresponding row if the object is expired.

    param instance: | object instance (must be given as a keyword arg)

  - `identity_key(class, row=row)`

    This form is similar to the class/tuple form, except is passed a database result row as a `RowProxy` object.

    E.g.:

        >>> row = engine.execute("select * from table where a=1 and b=2").first()
        >>> identity_key(MyClass, row=row)
        (<class '__main__.MyClass'>, (1, 2))

    param class: | mapped class (must be a positional argument)
    param row: | `RowProxy` row returned by a `ResultProxy` (must be given as a keyword arg)

`sqlalchemy.orm.util.` `polymorphic_union`(table_map, typecolname, aliasname='p_union', cast_nulls=True)

  Create a `UNION` statement used by a polymorphic mapper.

  See Concrete Table Inheritance for an example of how this is used.

  Parameters: | table_map – mapping of polymorphic identities to `Table` objects. typecolname – string name of a “discriminator” column, which will be derived from the query, producing the polymorphic identity for each row. If `None`, no polymorphic discriminator is generated. aliasname – name of the `alias()` construct generated. cast_nulls – if True, non-existent columns, which are represented as labeled NULLs, will be passed into CAST. This is a legacy behavior that is problematic on some backends such as Oracle - in which case it can be set to False.

class `sqlalchemy.orm.mapper.` `Mapper`(class_, local_table, properties=None, primary_key=None, non_primary=False, inherits=None, inherit_condition=None, inherit_foreign_keys=None, extension=None, order_by=False, always_refresh=False, version_id_col=None, version_id_generator=None, polymorphic_on=None, _polymorphic_map=None, polymorphic_identity=None, concrete=False, with_polymorphic=None, allow_partial_pks=True, batch=True, column_prefix=None, include_properties=None, exclude_properties=None, passive_updates=True, eager_defaults=False, legacy_is_orphan=False, _compiled_cache_size=100)

  Bases: `sqlalchemy.orm.interfaces._InspectionAttr`

  Define the correlation of class attributes to database table columns.

  The `Mapper` object is instantiated using the `mapper()` function. For information about instantiating new `Mapper` objects, see that function’s documentation.

  When `mapper()` is used explicitly to link a user defined class with table metadata, this is referred to as classical mapping. Modern SQLAlchemy usage tends to favor the `sqlalchemy.ext.declarative` extension for class configuration, which makes usage of `mapper()` behind the scenes.

  Given a particular class known to be mapped by the ORM, the `Mapper` which maintains it can be acquired using the `inspect()` function:

      from sqlalchemy import inspect

      mapper = inspect(MyClass)

  A class which was mapped by the `sqlalchemy.ext.declarative` extension will also have its mapper available via the `__mapper__` attribute.

  `__init__`(class_, local_table, properties=None, primary_key=None, non_primary=False, inherits=None, inherit_condition=None, inherit_foreign_keys=None, extension=None, order_by=False, always_refresh=False, version_id_col=None, version_id_generator=None, polymorphic_on=None, _polymorphic_map=None, polymorphic_identity=None, concrete=False, with_polymorphic=None, allow_partial_pks=True, batch=True, column_prefix=None, include_properties=None, exclude_properties=None, passive_updates=True, eager_defaults=False, legacy_is_orphan=False, _compiled_cache_size=100)

    Construct a new mapper.

    Mappers are normally constructed via the `mapper()` function. See for details.

  `add_properties`(dict_of_properties)

    Add the given dictionary of properties to this mapper, using add_property.

  `add_property`(key, prop)

    Add an individual MapperProperty to this mapper.

    If the mapper has not been configured yet, just adds the property to the initial properties dictionary sent to the constructor. If this Mapper has already been configured, then the given MapperProperty is configured immediately.

  `all_orm_descriptors`

    A namespace of all `_InspectionAttr` attributes associated with the mapped class.

    These attributes are in all cases Python descriptors associated with the mapped class or its superclasses.

    This namespace includes attributes that are mapped to the class as well as attributes declared by extension modules. It includes any Python descriptor type that inherits from `_InspectionAttr`. This includes `QueryableAttribute`, as well as extension types such as `hybrid_property`, `hybrid_method` and `AssociationProxy`.

    To distinguish between mapped attributes and extension attributes, the attribute `_InspectionAttr.extension_type` will refer to a constant that distinguishes between different extension types.

    When dealing with a `QueryableAttribute`, the `QueryableAttribute.property` attribute refers to the `MapperProperty` property, which is what you get when referring to the collection of mapped properties via `Mapper.attrs`.

    New in version 0.8.0.

    See also

    `Mapper.attrs`

  `attrs`

    A namespace of all `MapperProperty` objects associated this mapper.

    This is an object that provides each property based on its key name. For instance, the mapper for a `User` class which has `User.name` attribute would provide `mapper.attrs.name`, which would be the `ColumnProperty` representing the `name` column. The namespace object can also be iterated, which would yield each `MapperProperty`.

    `Mapper` has several pre-filtered views of this attribute which limit the types of properties returned, inclding `synonyms`, `column_attrs`, `relationships`, and `composites`.

    See also

    `Mapper.all_orm_descriptors`

  `base_mapper` = None

    The base-most `Mapper` in an inheritance chain.

    In a non-inheriting scenario, this attribute will always be this `Mapper`. In an inheritance scenario, it references the `Mapper` which is parent to all other `Mapper` objects in the inheritance chain.

    This is a read only attribute determined during mapper construction. Behavior is undefined if directly modified.

  `c` = None

    A synonym for `columns`.

  `cascade_iterator`(type_, state, halt_on=None)

    Iterate each element and its mapper in an object graph, for all relationships that meet the given cascade rule.

    Parameters: | type – The name of the cascade rule (i.e. save-update, delete, etc.) state – The lead InstanceState. child items will be processed per the relationships defined for this object’s mapper.

    the return value are object instances; this provides a strong reference so that they don’t fall out of scope immediately.

  `class_` = None

    The Python class which this `Mapper` maps.

    This is a read only attribute determined during mapper construction. Behavior is undefined if directly modified.

  `class_manager` = None

    The `ClassManager` which maintains event listeners and class-bound descriptors for this `Mapper`.

    This is a read only attribute determined during mapper construction. Behavior is undefined if directly modified.

  `column_attrs`

    Return a namespace of all `ColumnProperty` properties maintained by this `Mapper`.

    See also

    `Mapper.attrs` - namespace of all `MapperProperty` objects.

  `columns` = None

    A collection of `Column` or other scalar expression objects maintained by this `Mapper`.

    The collection behaves the same as that of the `c` attribute on any `Table` object, except that only those columns included in this mapping are present, and are keyed based on the attribute name defined in the mapping, not necessarily the `key` attribute of the `Column` itself. Additionally, scalar expressions mapped by `column_property()` are also present here.

    This is a read only attribute determined during mapper construction. Behavior is undefined if directly modified.

  `common_parent`(other)

    Return true if the given mapper shares a common inherited parent as this mapper.

  `compile`()

    Initialize the inter-mapper relationships of all mappers that

    Deprecated since version 0.7: `Mapper.compile()` is replaced by `configure_mappers()`

    have been constructed thus far.

  `compiled`

    Deprecated since version 0.7: `Mapper.compiled` is replaced by `Mapper.configured`

  `composites`

    Return a namespace of all `CompositeProperty` properties maintained by this `Mapper`.

    See also

    `Mapper.attrs` - namespace of all `MapperProperty` objects.

  `concrete` = None

    Represent `True` if this `Mapper` is a concrete inheritance mapper.

    This is a read only attribute determined during mapper construction. Behavior is undefined if directly modified.

  `configured` = None

    Represent `True` if this `Mapper` has been configured.

    This is a read only attribute determined during mapper construction. Behavior is undefined if directly modified.

    See also

    `configure_mappers()`.

  `entity`

    Part of the inspection API.

    Returns self.class_.

  `get_property`(key, _configure_mappers=True)

    return a MapperProperty associated with the given key.

  `get_property_by_column`(column)

    Given a `Column` object, return the `MapperProperty` which maps this column.

  `identity_key_from_instance`(instance)

    Return the identity key for the given instance, based on its primary key attributes.

    If the instance’s state is expired, calling this method will result in a database check to see if the object has been deleted. If the row no longer exists, `ObjectDeletedError` is raised.

    This value is typically also found on the instance state under the attribute name key.

  `identity_key_from_primary_key`(primary_key)

    Return an identity-map key for use in storing/retrieving an item from an identity map.

    Parameters: | primary_key – A list of values indicating the identifier.

  `identity_key_from_row`(row, adapter=None)

    Return an identity-map key for use in storing/retrieving an item from the identity map.

    Parameters: | row – A `RowProxy` instance. The columns which are mapped by this `Mapper` should be locatable in the row, preferably via the `Column` object directly (as is the case when a `select()` construct is executed), or via string names of the form `<tablename>_<colname>`.

  `inherits` = None

    References the `Mapper` which this `Mapper` inherits from, if any.

    This is a read only attribute determined during mapper construction. Behavior is undefined if directly modified.

  `is_mapper` = True

    Part of the inspection API.

  `isa`(other)

    Return True if the this mapper inherits from the given mapper.

  `iterate_properties`

    return an iterator of all MapperProperty objects.

  `local_table` = None

    The `Selectable` which this `Mapper` manages.

    Typically is an instance of `Table` or `Alias`. May also be `None`.

    The “local” table is the selectable that the `Mapper` is directly responsible for managing from an attribute access and flush perspective. For non-inheriting mappers, the local table is the same as the “mapped” table. For joined-table inheritance mappers, local_table will be the particular sub-table of the overall “join” which this `Mapper` represents. If this mapper is a single-table inheriting mapper, local_table will be `None`.

    See also

    `mapped_table`.

  `mapped_table` = None

    The `Selectable` to which this `Mapper` is mapped.

    Typically an instance of `Table`, `Join`, or `Alias`.

    The “mapped” table is the selectable that the mapper selects from during queries. For non-inheriting mappers, the mapped table is the same as the “local” table. For joined-table inheritance mappers, mapped_table references the full `Join` representing full rows for this particular subclass. For single-table inheritance mappers, mapped_table references the base table.

    See also

    `local_table`.

  `mapper`

    Part of the inspection API.

    Returns self.

  `non_primary` = None

    Represent `True` if this `Mapper` is a “non-primary” mapper, e.g. a mapper that is used only to selet rows but not for persistence management.

    This is a read only attribute determined during mapper construction. Behavior is undefined if directly modified.

  `polymorphic_identity` = None

    Represent an identifier which is matched against the `polymorphic_on` column during result row loading.

    Used only with inheritance, this object can be of any type which is comparable to the type of column represented by `polymorphic_on`.

    This is a read only attribute determined during mapper construction. Behavior is undefined if directly modified.

  `polymorphic_iterator`()

    Iterate through the collection including this mapper and all descendant mappers.

    This includes not just the immediately inheriting mappers but all their inheriting mappers as well.

    To iterate through an entire hierarchy, use `mapper.base_mapper.polymorphic_iterator()`.

  `polymorphic_map` = None

    A mapping of “polymorphic identity” identifiers mapped to `Mapper` instances, within an inheritance scenario.

    The identifiers can be of any type which is comparable to the type of column represented by `polymorphic_on`.

    An inheritance chain of mappers will all reference the same polymorphic map object. The object is used to correlate incoming result rows to target mappers.

    This is a read only attribute determined during mapper construction. Behavior is undefined if directly modified.

  `polymorphic_on` = None

    The `Column` or SQL expression specified as the `polymorphic_on` argument for this `Mapper`, within an inheritance scenario.

    This attribute is normally a `Column` instance but may also be an expression, such as one derived from `cast()`.

    This is a read only attribute determined during mapper construction. Behavior is undefined if directly modified.

  `primary_key` = None

    An iterable containing the collection of `Column` objects which comprise the ‘primary key’ of the mapped table, from the perspective of this `Mapper`.

    This list is against the selectable in `mapped_table`. In the case of inheriting mappers, some columns may be managed by a superclass mapper. For example, in the case of a `Join`, the primary key is determined by all of the primary key columns across all tables referenced by the `Join`.

    The list is also not necessarily the same as the primary key column collection associated with the underlying tables; the `Mapper` features a `primary_key` argument that can override what the `Mapper` considers as primary key columns.

    This is a read only attribute determined during mapper construction. Behavior is undefined if directly modified.

  `primary_key_from_instance`(instance)

    Return the list of primary key values for the given instance.

    If the instance’s state is expired, calling this method will result in a database check to see if the object has been deleted. If the row no longer exists, `ObjectDeletedError` is raised.

  `primary_mapper`()

    Return the primary mapper corresponding to this mapper’s class key (class).

  `relationships`

    Return a namespace of all `RelationshipProperty` properties maintained by this `Mapper`.

    See also

    `Mapper.attrs` - namespace of all `MapperProperty` objects.

  `selectable`

    The `select()` construct this `Mapper` selects from by default.

    Normally, this is equivalent to `mapped_table`, unless the `with_polymorphic` feature is in use, in which case the full “polymorphic” selectable is returned.

  `self_and_descendants`

    The collection including this mapper and all descendant mappers.

    This includes not just the immediately inheriting mappers but all their inheriting mappers as well.

  `single` = None

    Represent `True` if this `Mapper` is a single table inheritance mapper.

    `local_table` will be `None` if this flag is set.

    This is a read only attribute determined during mapper construction. Behavior is undefined if directly modified.

  `synonyms`

    Return a namespace of all `SynonymProperty` properties maintained by this `Mapper`.

    See also

    `Mapper.attrs` - namespace of all `MapperProperty` objects.

  `tables` = None

    An iterable containing the collection of `Table` objects which this `Mapper` is aware of.

    If the mapper is mapped to a `Join`, or an `Alias` representing a `Select`, the individual `Table` objects that comprise the full construct will be represented here.

    This is a read only attribute determined during mapper construction. Behavior is undefined if directly modified.

  `validators` = None

    An immutable dictionary of attributes which have been decorated using the `validates()` decorator.

    The dictionary contains string attribute names as keys mapped to the actual validation method.

  `with_polymorphic_mappers`

    The list of `Mapper` objects included in the default “polymorphic” query.